Evaluation of Data Mining Techniques and Its Fusion with IoT Enabled Smart Technologies for Effective Prediction of Available Parking Space

After experiencing the hard times of pandemic situations we learned that if could have a smart system can help us in automatic parking vehicles then it be great to society. This idea motivated carry out this current work. Though, nowadays, almost every application domain, IoT techniques are buzzword. also used achieve efficacy predicting free available space advance. But biggest challenge with is they generate numerous data, which makes its analysis intangible. It was realized fused outperforming data mining techniques, more efficient predictions performed. Thus, for purpose, main objective our paper firstly, select most appropriate technique, based on performance evaluation, and perform prediction advance by fusing techniques. Due busy schedule, drivers need get information about spaces using phones. With information, will easy park their vehicle exact location without wasting precious time maintain social distancing crowded areas too. Data play an important role space, extracting only relevant when applied given dataset. For comparative five such as Support Vector Machine, K- Nearest approach, Decision Tree, Random Forest, Ensemble learning approaches PK lot set Python language. calculation result anaconda (spyder) supportive tool. The outcome find technique give better results technologies improvised. Evaluation parameters finding best precision, recall, accuracy, F1-Score. numerical results, k-fold cross-validation method used. As empirical calculated Pk dataset, decision tree outperformed among all selected analysis.